Optimization of microfluidic synthesis of silver nanoparticles: A generic approach using machine learning

The properties of silver nanoparticles (AgNPs) are affected by various parameters, making optimisation of their synthesis a laborious task. This optimisation is facilitated in this work by concurrent use of a T-junction microfluidic system and machine learning ap-proach. The AgNPs are synthesized by reducing silver nitrate with tannic acid in the presence of trisodium citrate, which has a dual role in the reaction as reducing and sta-bilizing agent. The study uses a decision tree-guided design of experiment method for the size of AgNPs. The developed approach uses kinetic nucleation and growth constants derived from an independent set of experiments to account for chemistry of synthesis, the Reynolds number and the ratio of Dean number to Reynolds number to reveal effect of hydrodynamics and mixing within device and storage temperature to account for particle stability after collection. The obtained model was used to define a parameter space for additional experiments carried out to improve the model further. The numerical results illustrate that well-designed experiments can contribute more effectively to the devel-opment of different machine learning models (decision tree, random forest and XGBoost) as opposed to randomly added experiments. (c) 2023 The Authors. Published by Elsevier Ltd on behalf of Institution of Chemical Engineers. This is an open access article under the CC BY license (http://creative-commons.org/licenses/by/4.0/).

Automated summary

The synthesis of silver nanoparticles (AgNPs) is optimized using a T-junction microfluidic system and a machine learning approach. The reduction of silver nitrate with tannic acid in the presence of trisodium citrate is used to synthesize AgNPs. The study uses a decision tree-guided design of experiment method to determine the size of AgNPs.